A Multi-Swarm PSO Approach to Large-Scale Task Scheduling in a Sustainable Supply Chain Datacenter

Abstract

In recent years, supply chain management has become an incrementally vital part of industrial sectors, which affects the service quality and production efficiency of relevant enterprises. With the rapid development of cloud computing technology, a distributed datacenter plays a vital role in supply chain management of many infrastructures but suffers from high energy consumption and low service efficiency due to heavy allocation of massive calculation and analysis tasks. In this paper, an efficient multi-swarm particle swarm optimization approach is proposed based on load balancing. Both makespan and completion time variances of all resources have been minimized. An initialization scheme is also presented concerning the convergence rate and adaptive inertia weights. An open dataset from an Alibaba datacenter has been employed to resolve the uneven load events caused by inefficient supply chain arrangement between distributed tasks and resources. Two criteria, makespan and response time during task scheduling have been chosen for performance evaluation. According to the results, the proposed work can improve task scheduling efficiency and sustainability in distributed datacenters supporting diverse supply chain environments.